Analyzing Business Relations & Documents

class: title-slide

# ER018 - Analyzing Business Relations & Documents

## PVA3

### Analyse Räumlicher Daten in R

<br>
<br>
<br>
<br>
<br>
<br>
<br>
### FS 2024
<br>
### Prof. Dr. Jörg Schoder
.mycontacts[
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]

---
layout: true

<div style="position: absolute;left:400px;bottom:10px;font-size:9px"><svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M245.83 214.87l-33.22 17.28c-9.43-19.58-25.24-19.93-27.46-19.93-22.13 0-33.22 14.61-33.22 43.84 0 23.57 9.21 43.84 33.22 43.84 14.47 0 24.65-7.09 30.57-21.26l30.55 15.5c-6.17 11.51-25.69 38.98-65.1 38.98-22.6 0-73.96-10.32-73.96-77.05 0-58.69 43-77.06 72.63-77.06 30.72-.01 52.7 11.95 65.99 35.86zm143.05 0l-32.78 17.28c-9.5-19.77-25.72-19.93-27.9-19.93-22.14 0-33.22 14.61-33.22 43.84 0 23.55 9.23 43.84 33.22 43.84 14.45 0 24.65-7.09 30.54-21.26l31 15.5c-2.1 3.75-21.39 38.98-65.09 38.98-22.69 0-73.96-9.87-73.96-77.05 0-58.67 42.97-77.06 72.63-77.06 30.71-.01 52.58 11.95 65.56 35.86zM247.56 8.05C104.74 8.05 0 123.11 0 256.05c0 138.49 113.6 248 247.56 248 129.93 0 248.44-100.87 248.44-248 0-137.87-106.62-248-248.44-248zm.87 450.81c-112.54 0-203.7-93.04-203.7-202.81 0-105.42 85.43-203.27 203.72-203.27 112.53 0 202.82 89.46 202.82 203.26-.01 121.69-99.68 202.82-202.84 202.82z"/></svg>Prof. Dr. Jörg Schoder</div>

---
name: agenda
class: left

.blockquote[Agenda]

## Analyse Räumlicher Daten in R

* Überblick Bibliotheken

* Karten erstellen

* Räumliche Regression

* Räumliche Netzwerke

???

[Beispiel](https://crd230.github.io/lab9.html)

[Scott](https://www.esri.com/news/arcwatch/0410/lauren-scott.html)

---
class: inverse, center, middle

## Kartenerstellung in R

.blockquote[Vektordaten und shapefiles]

.blockquote[simple features und das sf-Paket]

.blockquote[Schweizer Geodaten]

.blockquote[Kartierung mit ggplot]

.blockquote[Interaktive Karten mit Leaflet]

---
class: left

.blockquote[]

## Dateiformate für räumliche Daten

.pull-left[
<img src="data:image/png;base64,#../../img/PVA3/Vektordaten_Dateiformate_(SurveyGyaan_2022)_medium.PNG" width="100%" style="display: block; margin: auto;" />
]
--
.pull-right[
<img src="data:image/png;base64,#../../img/PVA3/Rasterdaten_Dateiformate_(SurveyGyaan_2022)_medium.PNG" width="100%" style="display: block; margin: auto;" />
]

.quelle[Quelle: <a name=cite-surveygyaan_vector_2022></a>[Surveygyaan (2022)](#bib-surveygyaan_vector_2022), .]

---
class: left

.blockquote[sf-Paket]

## Warum das sf-Paket?

* Schnelles Lesen und Schreiben von Daten
--
* Verbesserte Leistung beim Plotten
--
* Verwendung von `sf`-Objekten (in der Regel) wie `data.frame`- oder `tibble-Objekte`
--
* konsistente und intuitive Verben als Funktionsnamen (beginnen mit `st_`)
--
* Kompatibilität mit tidyverse-Synatax und Verwendung mit Pipes

---
class: left

## Simple Karte

.panelset[
.panel[.panel-name[Top5 CH-Städte]

```r
library(tidyverse)
```

```
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
```

```r
my_in_file <- "CitiesCH_(simplemaps.com).csv"
tbl_citiesCH <- read_csv(xfun::from_root("data","raw","PVA3",my_in_file))
```

```
## Rows: 177 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): city, country, iso2, admin_name, capital
## dbl (4): lat, lng, population, population_proper
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

```r
tbl_citiesCH5 <- tbl_citiesCH %>% 
                          arrange(desc(population)) %>% 
                          slice_head(n=5)
tbl_citiesCH
```

```
## # A tibble: 177 × 9
##    city           lat   lng country     iso2  admin_name   capital population
##    <chr>        <dbl> <dbl> <chr>       <chr> <chr>        <chr>        <dbl>
##  1 Zürich        47.4  8.54 Switzerland CH    Zürich       admin       447082
##  2 Geneva        46.2  6.15 Switzerland CH    Genève       admin       203840
##  3 Basel         47.6  7.59 Switzerland CH    Basel-Stadt  admin       173552
##  4 Lausanne      46.5  6.63 Switzerland CH    Vaud         admin       141418
##  5 Bern          46.9  7.45 Switzerland CH    Bern         primary     134506
##  6 Winterthur    47.5  8.73 Switzerland CH    Zürich       minor       109775
##  7 Lucerne       47.0  8.3  Switzerland CH    Luzern       admin        81691
##  8 Sankt Gallen  47.4  9.37 Switzerland CH    Sankt Gallen admin        75833
##  9 Biel/Bienne   47.1  7.25 Switzerland CH    Bern         <NA>         55159
## 10 Bellinzona    46.2  9.03 Switzerland CH    Ticino       admin        43785
## # ℹ 167 more rows
## # ℹ 1 more variable: population_proper <dbl>
```
]
.panel[.panel-name[Einfache Abbildung]

```r
tbl_citiesCH5 %>% 
    ggplot(aes(x = lng, y = lat)) + geom_point()
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-4-1.png)
]
.panel[.panel-name[Labels ergänzen]

```r
tbl_citiesCH5 %>% 
    ggplot(aes(x = lng, y = lat)) + geom_point() + 
      geom_text(aes(label=city),hjust=1, vjust=-0.5) + 
      scale_x_continuous(limits = c(5.5,9))
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-5-1.png)
]
.panel[.panel-name[Gewichtung]

```r
tbl_citiesCH5 %>% 
    ggplot(aes(x = lng, y = lat)) + geom_point(aes(size=population)) + 
      geom_text(aes(label=city),hjust=1, vjust=-0.5) + 
      scale_x_continuous(limits = c(5.5,9))
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-6-1.png)
]
]

---
class: left

.blockquote[Kartenerstellung]

## Schweiz

```r
library(ggswissmaps)
ggswissmaps::maps2
```

```
## $g1b15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-1.png)

```
## 
## $g1g15_encl
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-2.png)

```
## 
## $g1g15_li
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-3.png)

```
## 
## $g1g15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-4.png)

```
## 
## $g1k15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-5.png)

```
## 
## $g1l15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-6.png)

```
## 
## $g1r15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-7.png)

```
## 
## $g1s15
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-7-8.png)

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-8-1.png)

???

[swissgd](https://github.com/zumbov2/swissgd)

[bfsMaps](https://cran.r-project.org/web/packages/bfsMaps/bfsMaps.pdf)

[RSwissMaps](https://github.com/zumbov2/RSwissMaps) aktuell noch nicht verfügbar für R4.4.0

---
class: left

```r
library(geojsonsf)
#sf <- geojson_sf("https://data.sbb.ch/api/explore/v2.1/catalog/datasets/linie-mit-polygon/exports/geojson")
sf_london <- geojson_sf(xfun::from_root("data","raw","PVA3","london_421.geojson"))
sf_zh <- geojson_sf(xfun::from_root("data","raw","PVA3","zurich-small_.geojson"))
p_zh <- sf_zh %>% ggplot2::ggplot() + geom_sf() + theme_void()
p_ld <- sf_london %>% ggplot2::ggplot() + geom_sf() + theme_void()
library(patchwork)
p_zh+p_ld
```

![](data:image/png;base64,#02_SpatialStats_in_R_files/figure-html/unnamed-chunk-9-1.png)

???

Quelle: [](https://cartographyvectors.com/map/421-london#google_vignette)

---
class: left

## Starbucks Zürich

```r
library(tidyverse)
tbl_starbucks <- read_csv(xfun::from_root("data","raw","PVA3","starbucks_(kaggle).zip"))
starbucks_zh <- tbl_starbucks %>% 
                        filter(City=="Zurich"&Country=="CH")
```

---
class: left

.blockquote[Räumliche Autokorrelation]

## Kennzahlen Räumliche Autokorrelation

* Moran

???

Griffith, Enzyklopädie GIS

---
class: inverse, center, middle

## Räumliche Autokorrelation

.blockquote[]

???

---
class: inverse, center, middle

## Räumliche Statistik vs. Geostatistik

.blockquote[]

---
class: left

## Zusammenfassung

.quelle[Quelle: <a name=cite-seguret_geostatistics_2021></a>[Séguret and Emery (2021)](#bib-seguret_geostatistics_2021), S. xxi.]

---
class: inverse,center,middle

# Wir brauchen eine Pause.

---

background-image: url("data:image/png;base64,#http://bit.ly/cs631-donkey")
background-size: 80%

---
class: left

## Quellenverzeichnis

.ref-slide[
<a name=bib-seguret_geostatistics_2021></a>[Séguret, S. A. and X.
Emery](#cite-seguret_geostatistics_2021) (2021). _Geostatistics for the
Mining Industry: Applications to Porphyry Copper Deposits_. Boca Raton
London New York: CRC Press, Taylor and Francis Group. ISBN:
978-0-367-50575-2.

<a
name=bib-surveygyaan_vector_2022></a>[Surveygyaan](#cite-surveygyaan_vector_2022)
(2022). _Vector Data in GIS_. (Visited on Mai. 21, 2024).
]